This SBIR project is developing the first geostatistical software to offer tools that are specifically designed for the analysis of health data (e.g. cancer rates), providing: description of spatial patterns of disease and identification of scales of variability, estimation and mapping of risk from empirical frequencies measured over different supports, spatial interpolation and stochastic modeling of exposure data, and the investigation and visualization of scale-dependent relationships between exposure and health data. This project will accomplish 6 aims: 1. Conduct a requirements analysis to identify the spatial methods and functionality to incorporate into the software. 2. Develop and test innovative geostatistical techniques for spatial filtering of cancer rates and analysis of scale-dependent correlations. 3. Develop a novel approach for change of support (i.e. spatial disaggregation or side-scaling), quantification and propagation of uncertainty through local cluster analysis and ecological regression. 4. Build and test a complete set of functionalities based on results of research and simulation studies, which will be incorporated into Biomedware's space-time visualization and analysis technology. 5. Apply the software and methods to demonstrate the approach and its unique benefits for exposure and health risk assessment. 6. Create instructional materials, including a short course, to foster the adoption of this approach in health science. Feasibility of this project was demonstrated in the Phase I. This Phase II project will accomplish aims three through six. These technologic, scientific and commercial innovations will revolutionize our ability to interpret geographic variation in cancer mortality, understand relationships between cancers at different spatial scales and over different supports, and to quantify risk factors. [unreadable] [unreadable] [unreadable]